Altan Kocyigit

On determining cluster size of randomly deployed heterogeneous WSNs

Clustering is an efficient method to solve scalability problems and energy consumption challenges. For this reason it is widely exploited in Wireless Sensor Network (WSN) applications. It is very critical to determine the number of required clusterheads and thus the overall cost of WSNs while satisfying the desired level of coverage. Our objective is to study cluster size, i.e., how much a clusterhead together with sensors can cover a region when all the devices in a WSN are deployed randomly. Therefore, it is possible to compute the required number of nodes of each type for given network parameters.

A hybrid approach to minimize state space explosion problem for the solution of two stage tandem queues

Two stage open queuing networks are used for modeling the subsystem-behaviour in computers and communication networks, mass storage devices, memory servers, and queuing analysis of wireless mobile cellular networks. The queuing analysis of wireless systems is essential in order to quantify the impact of different factors on quality of service (QoS); performance measures so that wireless protocols can be designed and/or tuned in an optimal manner. In that sense two stage open queuing systems are particularly important to model handoff phenomena, especially for the integration of two different systems such as cellular and wireless local area networks (WLANs). Analytical solutions for two-dimensional Markov processes suffer from the state space explosion problem. The numerical difficulties caused by large state spaces, make it difficult to handle multiple servers at the second stage of a tandem queuing system together with server failures and repairs. This study presents a new approach to analytical modeling of open networks offering improvements in alleviating this problem. The proposed solution is a hybrid version, which combines well known spectral expansion, and hierarchical Markov reward rate approaches. Using this approach, two-stage open networks with multiple servers, break-downs, and repairs at the second stage and feedback can be modeled as three-dimensional Markov processes and solved for performability measures. Comparative results show that the new algorithm used for solution, provides a high degree of accuracy, and it is computationally more efficient than the existing approaches. The proposed model is capable of solving other three-dimensional Markov processes.

Granular best match algorithm for context-aware computing systems

In order to be context-aware, a system or application should adapt its behavior according to current context, changing over time. Current context is acquired by some context provision mechanisms like sensors or other applications. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA - context-aware personal reminder agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the users intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method

Modelling and analysis of vertical handover in highly mobile environments

Various types of wireless networks have been developed and deployed including 3G, WLAN, WiMAX, LTE and LTE Advanced. User connectivity and network performance can be improved using vertical handover techniques which involve switching between available networks in heterogeneous environments. In this respect, recently there has been an increased interest in the integration of cellular and WLAN systems. In this study, the integrated heterogeneous wireless systems are modelled using two-stage open queuing systems. The proposed analytical model builds a framework for acceptable levels of QoS in heterogeneous environments. This paper gives the detailed analysis of the integrated cellular/WLAN systems based on the deployment of guard channels and buffering at base station for highly mobile users in the cellular systems. Numerical results are obtained using the exact spectral expansion solution approach. These results are then analysed in terms of operational spaces and are shown to be useful for vertical handover decision management.

An optimal network dimensioning and initial energy assignment minimizing the monetary cost of a heterogeneous WSN

In this paper, a novel method is proposed to dimension a randomly deployed heterogeneous Wireless Sensor Network (WSN) of minimum monetary cost satisfying minimum coverage and minimum lifetime requirements. We consider WSNs consisting of two different types of nodes clusterheads and ordinary sensor nodes, randomly deployed over a sensing field. All devices are assumed to be stationary and have identical sensing capabilities. However, the clusterheads are more energetic and powerful in terms of processing and communication capabilities compared to sensor nodes. For such a network, finding minimum cost WSN problem is not a trivial one, since the distribution of the mixture of two different types of devices and the batteries with different initial energies in each type of device primarily determine the monetary cost of a WSN. Therefore, we formulated an optimization problem to minimize the monetary cost of a WSN for given coverage and lifetime requirements. The proposed optimization problem is solved for a certain scenario and the solution is validated by computer simulations.

A Generic Analytical Modelling Approach for Performance Evaluation of the Handover Schemes in Heterogeneous Environments

In recent years, there has been an increased interest in the integration of different technologies in heterogeneous environments. Modelling heterogeneous systems is a complex task and handover schemes should consider issues such as network coverage, mobility, and Quality of Service (QoS). Analytical models are useful to deal with this complexity. This paper presents a generic framework to model handover in heterogeneous environments for performance evaluation of different handover schemes. The model developed considers mobility of the users, coverage radius of the networks as well as decision making probabilities about handover between the two networks. The interaction between cellular mobile technologies is considered and handover between a WLAN and a cellular system is analysed using the proposed model. Two stage open networks can be used to model this kind of integrated systems. The cellular mobile technologies are modelled as a multi-channel queuing system while the WLAN is depicted as a single channel queuing system, both with finite buffers. The exact spectral expansion method is employed to solve the models. Simulation is also employed for the proposed systems and used for validation of the accuracy of the proposed models. Numerical results are presented for mean queue length and blocking probabilities for each system. The results of this study show that QoS measures of such systems can be evaluated efficiently and accurately, using the proposed analytical model and its solution. In addition, it can be used as a framework in heterogeneous environments and can also be adapted to various types of networks in interaction.

An Early Software Effort Estimation Method Based on Use Cases and Conceptual Classes

It is an important issue in the software industry to predict how much effort will be required for a software project as early as possible. Software size is one of the commonly used attributes in effort estimation. In this paper, we propose an early software size and effort estimation method based on conceptual model of the problem domain. Our method utilizes the noteworthy domain concepts identified mainly from the use cases written in the requirements phase of the software development lifecycle. In order to develop the model and evaluate its prediction quality, the use cases written and the effort data collected for 14 industrial software development projects of a CMMI level 3 certified defense industry company have been used. Evaluation results reveal a high correlation between the number of conceptual classes identified (i.e., domain objects) during the requirements analysis, the number of classes constituting the resulting software and the actual effort spent. Moreover, we have used the use case point (UCP) method to estimate the effort needed for each project and compared the results of UCP analysis with the results obtained with our method. The comparisons have shown that, for the projects considered, our method gives a better effort estimation compared to the effort estimated by using the UCP method.

Granular Best Match Algorithm for Context-Aware Computing Systems

In order to be context-aware, a system or application should adapt its behavior according to current context, changing over time. Current context is acquired by some context provision mechanisms like sensors or other applications. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA - context-aware personal reminder agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the users intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method

A Cloud Based Architecture for Distributed Real Time Processing of Continuous Queries

With the rapid pace of technological advancements in smart device, sensor and actuator technologies, the Internet of Things (IoT) domain has received significant attention. These advances have brought us closer to the ubiquitous computing vision. However, in order to fully realize this vision, devices and applications should rapidly adapt to the changes in the environment and other nearby devices. Most of the existing applications store collected data in a data store and allow users to query stored data to notice and react to such changes. Many of the applications utilize cloud and big data technologies for scalability. Nevertheless, the responsiveness of such IoT applications is largely limited due to polling based queries. Therefore, in this paper, we propose a centrally managed distributed infrastructure based on the state of the art big data technologies. We focus primarily on the problem of how to process multiple continuous queries in real time and notify users timely. Our main contribution is specifying a generic and scalable architecture to process a multitude of real time queries. We provide a prototype implementation to demonstrate the applicability of the approach. The scalability of the architecture is evaluated by conducting several experiments on the prototype implementation.

A Mobile Computing Framework Based on Adaptive Mobile Code Offloading

Smart phones are not capable of competing against their desktop counterparts or servers in terms of CPU speed, battery, memory and storage. However, a mobile device can transparently use cloud resources by employing an offloading mechanism. Offloading enables mobile devices to run computation intensive applications such as object recognition, Optical Character Recognition (OCR) and augmented reality. In this paper, an Inversion of Control (IoC) based offloading technique is proposed in order to overcome shortcomings and limitations of current approaches in the literature. A sample application has been implemented by using the proposed technique. The results show that the proposed offloading technique leads to energy savings of 66% to 81% and execution time savings by 76% to 81% with a small computational overhead.